This makes sense to me.  Nonetheless I am often astonished by the frequent tendency among many corporate decision-makers to conflate the application of some management tool with a fabulous consultant-ese moniker into a “magic bullet” that will effortlessly change the organization overnight from a laggard to a market driving leader.  Then, as egregiously as they confer magic powers on the tools, after a few fiscal quarters the decision-makers realize they are not getting sustainable performance improvement, decide in their infinite wisdom that the inherent inadequacy of the tools is at fault, and consign them to the trash heap of unrealized expectations.

meaningful tools or random noise?

This misguided tendency – to ascribe awesome powers to something and then discard it for the wrong reasons – brings to mind one of my favorite management lessons: a timeless exercise developed by W. Edwards Deming called the Red Beads Experiment (actually, what I call “timeless” Deming himself calls “a stupid experiment you will never forget”).  Deming was one of the founding fathers of Statistical Process Control, itself a prototype of the management tools that abound in our age, and something of an iconic hero for several generations of Japanese business leaders dating back to the 1950s.  The phrase “you can’t improve what you can’t measure” is often attributed to Deming, though not always in the right context.  A more accurate reflection of his philosophy would perhaps be “measuring the wrong thing is much worse than not measuring at all”, and that brings us back to the Red Bead Experiment and its lessons for managers of today in the use and misuse of performance management tools.

The Red Bead Experiment is quite simple. It starts with the simulation of a factory tasked with the sole objective of making white beads.  The factory’s customers will only accept white beads; beads of any other color are rejected as unacceptable.  In the simulated experiment we represent the operations of the factory with a sampling device that contains a total population of 80% white beads and 20% red beads.  The red beads in turn represent defects caused by one or more organizational or operational flaws (such as poor design, faulty machinery, improper order communication, inadequate resource allocation, shoddy quality control and similar shortcomings).

In the first step of the experiment a manager selects an operational team consisting of six workers, two quality inspectors and a chief inspector.  This team simulates the factory’s “production process” as follows: every day, each worker draws an independent sample of 50 beads from the sampling device.  When a sample is drawn each inspector will separately record the number of red beads in the draw and report that number to the chief inspector, who will record the results.  This initial simulation can go on for several days, i.e. by the end of, say, four days each of the six workers will have drawn four independent samples of 50 beads and the number of red beads (i.e. “defects”) will be recorded for each draw and the results will be averaged to produce a consolidated “performance result” for each worker over this period.

At this point the experiment calls for the manager to employ a combination of suggestions, processes, incentives, threats and so forth (which we can think of as “management tools”) to extract better performance from the workers.  For example the manager may tell one worker whose “defect score” was higher than average to use a different technique when using the sampling paddle to extract the 50 beads (“flip your wrist a bit to the right – yes, like that!”), while telling another whose draw of red beads was lower than that of the group as a whole to “keep up the good work, expand your knowledge of white beads and there will be a year-end bonus in store for you”.  The experiment will repeat over several further iterations, each recording different performance results and with the manager constantly discarding and implementing performance tools in response to the results achieved.

The point of all these performance improvement devices, of course, is that they are pointless: the “system” from which the samples are drawn contains 80% white beads and 20% red beads. Actual results will simply reflect random, independent deviations from this 80/20 distribution and over successive iterations the average of all the draws will converge towards that 80/20 split.  The real underlying message is that measuring the effect of any given performance tool (whether it be based on incentive, threat, knowledge or process improvement) is useless without a grounded understanding and (where possible) measurement of the system itself.  In the language of Deming’s experiment, if you want to optimize the system for minimal red bead production then figure out how to change the 80/20 stasis at the system’s heart – then use appropriate management performance tools to align the activities of all the organizational resources in a self-reinforcing manner to achieve this desired strategic outcome.

Management tools have proliferated in the years since Deming’s heyday, and many of them offer the potential for real performance improvement. For example, organizations have the ability to surgically manipulate the operational levers at their disposal through performance approaches such as Supply Chain Management on the cost side and Price Optimization on the revenue side.  However, translating the benefits from such approaches into sustainable competitive advantage requires something more than the mere implementation of these (or other) tools: a granular understanding of each activity underlying the organization’s supply and demand chains, an ability to disentangle and measure the impact of numerous variables on cost and revenue performance, a deep and holistic understanding of the constraints presented by different management and operational decisions, and a transparent view of the full portfolio of activities from all the silos and subsystems throughout the organization.  That is no easy accomplishment – which of course is why sustainable advantage is no easy thing.

At the same time, though, a second major event appears to be unfolding:  the emergence of the economics of “free,” or “freeconomics” as provocatively described by Chris Anderson of Wired magazine in his recently published book “Free: The Future of a Radical Price.”  “Free” in Anderson’s formulation is the notion that the near-zero cost of doing business online turns upside down the conventional notion of economics as the science of parsimonious choices under conditions of scarcity.  The “economics of abundance” in Anderson’s phraseology may filter through the prism of our traditional understanding of markets as being good news for cash-strapped consumers (more stuff for which I don’t have to pay money) and bad news for suppliers of goods and services (“free” doesn’t sound like a price that will shore up my profit margins).

But is that right?  I would argue differently: the playing field where Freetopia meets the Great Deleveraging presents unique opportunities for enterprises that are able to use scientific methods to figure out the detailed contours of this new environment.  Household dollars are hard to come by.  But there are other things of value that factor into Freetopian economics: things like time, attention and reputation.  The key challenge for organizations is to figure out what these things are, who cares about them, where they fit into the picture and how to quantify them for optimal outcome.

I distill the following principal arguments from Anderson’s work: (a) the cost of doing business online is nearly zero; (b) transactions in Freetopia are not classical binary exchanges between a single buyer and a single seller, but rather involve a mix of parties where the exchange of cash is only a part of the value equation; and (c) some of the parties to the transaction are willing to offer some things for free in exchange for other things that confer some other value notion.  These complex multiparty transactions involve exchanges of product, service, cash, convenience, labor, information, gifts, reputation and awareness. In other words, Freetopia is not synonymous with free lunch (though, enjoyably, we discover in Anderson’s book the origins of this phrase as a value proposition used by San Francisco saloons in the late 1800s: anyone paying for a beer got a “free” lunch to go with it).

What this prompts us to do is to think in new ways about how our customers’ demand curves fit into that complex web of interests.  What are the components of the total cost borne by the customer in any given transaction, and what are the terms of value?  How valuable to the customer is a reduction in the cost of search?  What would induce the customer to pay more for A while getting B and C for nothing, or perhaps bartering a service (such as writing a review or filling out a questionnaire) that would benefit some other party to the transaction who would then subsidize part of the cash price of A to make it more appealing to the customer?  These are the types of opportunities that emerge on this new playing field.

The added complexity posed by these non-traditional transaction webs suggests that going by gut instinct alone will not suffice for organizations trying to figure out how to optimally supply their customers’ demand curves.  Nor, however, will the methods embedded in earlier generations of revenue optimization solutions be up to the task.  As Freetopia moves more into the mainstream of our economic lives the scientific methods that help us uncover the most important insights will need to do more than apply conventional optimization algorithms to historical daily prices.  At Sentrana our focus is on achieving mastery at the micromarket level – disentangling all the variables that connote what matters to a given customer at a given node in a given transaction opportunity.  As we look into the kind of future that Freetopia presages, we see an increased urgency for nuanced clarity and a growing role for scientific micromarketing – not as a one-off management tool but something at the strategic core of making the most from the opportunities this daunting – but potentially lucrative new world – will provide.